This 2002 book investigates the opportunities in building
intelligent decision support systems offered by multi-agent
distributed probabilistic reasoning. Probabilistic reasoning with
graphical models, also known as Bayesian networks or belief
networks, has become increasingly an active field of research and
practice in artificial intelligence, operations research and
statistics. The success of this technique in modeling intelligent
decision support systems under the centralized and single-agent
paradigm has been striking. Yang Xiang extends graphical dependence
models to the distributed and multi-agent paradigm. He identifies
the major technical challenges involved in such an endeavor and
presents the results. The framework developed in the book allows
distributed representation of uncertain knowledge on a large and
complex environment embedded in multiple cooperative agents, and
effective, exact and distributed probabilistic inference.
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